Real-time polymerase chain reaction (RT-PCR) technology, as presently practiced, relies upon the accurate detection of fluorescent emission signals above an initial baseline. The baseline signal can represent a combination of spurious or unwanted signal contributions such as the residual fluorescence contributed by the plastic or other material of a sample plate, the fluorescence of a running buffer or other non-reactant liquid material, noise in the optical detector or detection electronics, or some other source of background signal noise or detection floor that is not a product of the amplification or other reaction. In various known RT-PCR implementations, better accuracy in the detection of the amplification signal, and hence original sample quantity, is frequently sought by characterizing the baseline floor over the first few PCR cycles, or pre-signal detection cycles, and then subtracting the baseline from the detected emissions once an inflection point into the exponential region has been reached. In general, a RT-PCR emission or other amplification graph, chart, or profile typically displays three sections or regions: an initial baseline region, an exponential region, and a plateau region. An example of this is shown in the illustration in FIG. 1. The baseline region can display a linear, or approximately linear, or other form over the first several cycles, as reaction chemistries have not liberated enough marker dye to rise over the detected background. The next, exponential region represents the rise of amplification product over the noise or background floor, as the PCR reaction kinetics come into force. The plateau region typically exhibits a final flattening or tapering of detected emission intensities, as reagents are exhausted. The combined amplification profile usually resembles a sigmoid or S-shape. Typically, RT-PCR systems determine a threshold cycle (CT) which represents the cycle point at which the exponential threshold is reached. From that parameter the original sample quantity can be back-calculated, using standard curves.
Known baselining techniques involve the adjustment or normalization of the detected emission signal by subtracting the identified baseline in the first few cycles from the detected fluorescent intensities of the RT-PCR marker dyes in later cycles, to sharpen the accuracy of the absolute value of detected emission data in the exponential and/or plateau regions of the amplification profile. Baselining that relies upon a subtraction operation to perform normalization can, however, cause certain effects in the resulting modified or normalized data. For one, if a baselining operation is performed on a per-filter or per-dye wavelength basis, the baselining operation can determine different baselines for different filters or dyes, which after subtraction from the emission data lead to differing results for different detected channels. For another, if individual wells of a sample plate or other support or container are individually processed to create separate baselines on a per-well basis, the set of resulting baselined signals can be at a different scale or level. Furthermore, known baselining techniques involve the initial computation of baseline levels over the first few cycles, before exponential or plateau-region reactions takes place. Subtracting those baseline levels from a set of exponential or plateau-region data captured at a later point can introduce inaccuracies, for instance if the baseline level drifts over later cycles. A need exists for baseline and related techniques that address these and other issues.